US7668334B2 - Conditioning imagery to better receive steganographic encoding - Google Patents
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- US7668334B2 US7668334B2 US11/143,088 US14308805A US7668334B2 US 7668334 B2 US7668334 B2 US 7668334B2 US 14308805 A US14308805 A US 14308805A US 7668334 B2 US7668334 B2 US 7668334B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
- G06T1/0021—Image watermarking
- G06T1/005—Robust watermarking, e.g. average attack or collusion attack resistant
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- G—PHYSICS
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- G06T11/00—2D [Two Dimensional] image generation
- G06T11/001—Texturing; Colouring; Generation of texture or colour
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- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
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- H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
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- H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
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- H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
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- H04N1/60—Colour correction or control
- H04N1/6002—Corrections within particular colour systems
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- H04N1/46—Colour picture communication systems
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- H04N2201/3225—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title of data relating to an image, a page or a document
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- H04N2201/3228—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title of data relating to an image, a page or a document of identification information or the like, e.g. ID code, index, title, part of an image, reduced-size image further additional information (metadata) being comprised in the identification information
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- H04N2201/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N2201/3201—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N2201/3225—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title of data relating to an image, a page or a document
- H04N2201/3233—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title of data relating to an image, a page or a document of authentication information, e.g. digital signature, watermark
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- H04N2201/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
- H04N2201/3201—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
- H04N2201/3269—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title of machine readable codes or marks, e.g. bar codes or glyphs
- H04N2201/327—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title of machine readable codes or marks, e.g. bar codes or glyphs which are undetectable to the naked eye, e.g. embedded codes
Definitions
- the present invention relates generally to digital watermarking.
- the present invention relates to modifying imagery to better receive digital watermarking.
- the present invention provides methods and apparatus to modify an image to better receive digital watermarking.
- the methods and apparatus can be integrated into image workflows and only modify those images that may be sub par in terms of their ability to receive and hide digital watermarking.
- One example workflow is image capture for identification documents (e.g., at DMV station or passport application process).
- Another example is a photography studio during its print (or proofs) process.
- Digital watermarking technology a form of steganography, encompasses a great variety of techniques by which plural bits of digital data are hidden in some other object, preferably without leaving human-apparent evidence of alteration.
- Digital watermarking may be used to modify media content to embed a machine-readable code into the media content.
- the media may be modified such that the embedded code is imperceptible or nearly imperceptible to the user, yet may be detected through an automated detection process.
- the data may be encoded in the form of surface texturing or printing. Such marking can be detected from optical scan data, e.g., from a scanner or web cam.
- the data may be encoded as slight variations in sample values.
- the object is represented in a so-called orthogonal domain (also termed “non-perceptual,” e.g., MPEG, DCT, wavelet, etc.)
- the data may be encoded as slight variations in quantization values or levels.
- the assignee's U.S. Pat. Nos. 6,122,403 and 6,614,914 are illustrative of certain watermarking technologies. Still further techniques are known to those of ordinary skill in the watermarking art.
- Digital watermarking systems typically have two primary components: an embedding component that embeds a watermark in the media content, and a reading component that detects and reads the embedded watermark.
- the embedding component embeds a watermark pattern by altering data samples of the media content.
- the reading component analyzes content to detect whether a watermark pattern is present. In applications where the watermark encodes information, the reading component extracts this information from the detected watermark.
- the decoding process may be unable to recognize and decode the watermark.
- the watermark can convey a reference signal or “orientation component.”
- the orientation component is of such a character as to permit its detection even in the presence of relatively severe distortion.
- the attributes of the distorted reference signal can be used to quantify the content's distortion. Watermark decoding can then proceed—informed by information about the particular distortion present.
- an orientation component comprises a constellation of quasi-impulse functions in the Fourier magnitude domain, each with pseudorandom phase.
- the watermark decoder converts the watermarked image to the Fourier magnitude domain and then performs a log polar resampling of the Fourier magnitude image.
- a generalized matched filter correlates a known orientation signal with the re-sampled watermarked signal to find the rotation and scale parameters providing the highest correlation.
- the watermark decoder performs additional correlation operations between the phase information of the known orientation signal and the watermarked signal to determine translation parameters, which identify the origin of the watermark message signal. Having determined the rotation, scale and translation of the watermark signal, the reader then adjusts the image data to compensate for this distortion, and extracts the watermark message signal.
- One aspect of the present invention is a method to condition an image prior to receiving steganographic encoding.
- the process includes receiving an image and subjecting the image to an approximation of an expected workflow process.
- the subjected image is evaluated to determine whether the image includes characteristics that are conducive to receive steganographic encoding in view of the expected workflow process. If the digital image is not conducive, the image is modified.
- Another aspect of the present invention is a method to analyze a digital image to determine whether the digital image will be a suitable host to receive digital watermarking.
- the method includes processing a digital image in accordance with an approximation of an expected workflow process, and analyzing the processed digital image to determine whether the digital image forms a suitable host to receive digital watermarking. If the digital image is not suitable, the digital image is modified to better receive digital watermark in anticipation of the expected workflow process.
- FIG. 1A illustrates an example image workflow
- FIG. 1B illustrates image analysis and modification for the workflow of FIG. 1A .
- FIG. 2 illustrates a response curve for an expected image capture device and printer.
- FIG. 3A illustrates an image including areas susceptible to washing out with workflow processing
- FIG. 3B illustrates a corresponding luminosity histogram
- FIG. 3C illustrates the FIG. 3A image after processing according to the FIG. 2 curve.
- FIG. 4 illustrates tone correction for washed-out images.
- FIG. 5A illustrates the FIG. 3A image after applying the tone correction of FIG. 4 ;
- FIG. 5B illustrates a corresponding luminosity histogram;
- FIG. 5C illustrates the FIG. 5A image after processing according to the FIG. 2 curve.
- FIG. 6A illustrates an image including relatively darker image regions
- FIG. 6B illustrates a corresponding luminosity histogram
- FIG. 6C illustrates the FIG. 6A image after processing according to the FIG. 2 curve.
- FIG. 7 illustrates tone correction for images including dark or shadow regions.
- FIG. 8 illustrates an image including both washed-out regions and relatively darker image regions.
- FIG. 9 illustrates tone correction for an image including both washed-out regions and darker image regions.
- FIG. 10 is a flow diagram for image analysis.
- FIG. 11 illustrates a cumulative histogram of good (or acceptable) images
- FIG. 12 illustrates a cumulative histogram of washed-out images
- FIG. 13 illustrates a cumulative histogram of images including relatively darker regions.
- FIGS. 14A and 14B illustrate an image include a relatively high level of noise
- FIG. 14C illustrates a corresponding histogram
- FIGS. 15A and 15B illustrate corresponding images—after smoothing of the FIGS. 14A and 14B images—including a relatively lower level of noise
- FIG. 15C illustrates a corresponding histogram
- FIG. 16 illustrates an imaging system characteristic response curve
- FIG. 17 illustrates an image analysis algorithm flow diagram.
- FIG. 18 illustrates an image with significant highlight content and a corresponding histogram.
- the histogram plots image grayscale values against percentages of pixels in the image.
- FIG. 19 illustrates a tone correction calculated for an image (Appendix A, FIG. 3 ) with significant highlight content.
- FIG. 20 illustrates an image with significant highlight content and its histogram after tone correction of FIG. 19 .
- the histogram shows how pixels values have moved relative to the histogram in FIG. 18 .
- FIG. 21 illustrates an error rate for a watermark including 96 payload bits.
- FIG. 22 illustrates SNR distributions of two datasets.
- FIG. 1A illustrates a conventional image workflow.
- a digital image 10 is received.
- the image is captured by a digital camera, converted to a digital image from conventional film, or obtained via an optical scanning device.
- the digital image 10 is printed by printer 12 onto a substrate 14 .
- Printed substrate 14 is expected to be eventually captured by an image capture device 16 (e.g., an optical sensor, scanner, digital camera, cell phone camera, etc.).
- the image capture device 16 produces a digital representation 18 of substrate 14 , including a representation of digital image 10 printed thereon.
- all components of a digital watermark embedder and reader systems are preferably modeled as approximately linear, and all watermarked images read in a similar manner (e.g., assuming equal watermark embedding strength).
- Non-linearity of a capture and print device can be compensated for by characterizing the devices and compensating for their response (see, e.g., Assignee's U.S. Pat. No. 6,700,995 and U.S. patent application Ser. No. 10/954,632, filed Sep. 29, 2004, which are both herein incorporated by reference.).
- a captured image is preferably analyzed for its ability to carry and conceal a watermark, taking into account an anticipated response of an output printer and capture device.
- image correction or modification is applied.
- the correction preferably minimizes a visual impact attributable to watermarking, but still allows an embedded watermark to read by ensuring that about 50-85% of the pixels in the image are in an approximately linear section of a print device. (We most prefer that at least 70% of the pixels fall within the approximately linear section of the print device.).
- Image analysis 20 determines whether the digital image 10 , if subjected to an anticipated print process (e.g., process 12 ) and eventual image capture (e.g., process 16 ), will provide a suitable environment for digital watermarking. If the digital image is acceptable, flow continues to a digital watermark embedder 11 , where the digital image is embedded with one or more digital watermarks. Otherwise the digital image is modified 22 to provide more favorable image characteristics. A modified digital image is provided to the digital watermark embedder 11 . The embedded image is printed.
- an anticipated print process e.g., process 12
- eventual image capture e.g., process 16
- analysis 20 and modification 22 can be manually preformed, e.g., via photo editing software like Adobe's PhotoShop, we prefer an automated the process.
- the analysis 20 and modification 22 can be dedicated software modules and incorporated into an SDK or application program.).
- FIG. 2 illustrates a printer/scanner curve, which models anticipated characteristics of (and/or corruption introduced by) printer 12 and scanner 16 .
- a response curve can be generated by printing a range of test patterns on the printer 12 and scanning in the printed test patterns via scanner 16 .
- the scanned in test patterns are analyzed to model the workflow process (e.g., printer 12 and scanner 16 ). Results of this process are represented in the response curve FIG. 2 (e.g., in terms of luminance, or defining a range of pixel values that fall within a linear region).
- the workflow response e.g., in terms of luminance, or defining a range of pixel values that fall within a linear region.
- FIG. 3A After passing through (or analysis relative to) the printer/scanner response curve ( FIG. 2 ), a resulting image will appear washed-out or faded as shown in FIG. 3C .
- a luminosity histogram corresponding to FIG. 3A is shown in FIG. 3B .
- the percentage of pixels in the range from low_lum to high_lum is only about 16% in this case.
- the large number of pixels in the highlight or high luminance areas creates an image which appears washed-out.
- tone correction is applied to bring a predetermined number of image pixels into the linear region of the printer/scanner (e.g., between the dashed lines shown in FIG. 2 ). While we prefer to bring at least 70% of the pixels within this linear region, we can accept a range having a lower boundary of approximately 50% of pixels within the linear region.
- the dashed line in FIG. 4 represents preferred image tone, while the solid line represents the actual image tone.
- the FIG. 3A image is tone corrected, e.g., pixel values are reduced. This process moves the solid line more toward the dashed line in FIG. 4 .
- This tone correction process brings a predetermined number of pixels (e.g., 70%) into the desired linear range. For example, a predetermined number of pixels now fall between the dashed lines of FIG. 2 .
- the tone correction is preferably applied to the red, green and blue channels (or CYMK channels, etc.). Tone correction can be automated using conventional software such as Adobe's PhotoShop. Of course, software modules (e.g., C++) can be written to accomplish the same task.
- FIG. 5A A resulting image—after tone correction—is shown in FIG. 5A .
- the FIG. 5A image is deemed to have acceptable characteristics.
- FIG. 5A luminosity features are shown in FIG. 5B .
- a comparison between FIG. 5B and FIG. 3B helps to illustrate that the overall highlight pixels in FIG. 5A have been reduced compared to FIG. 3A ).
- We determine that the FIG. 5C image is better able to host a watermark—in comparison to FIG. 3 C—and is also more visually pleasing.
- FIG. 6A The image represented in FIG. 6A is passed through (or analyzed relative to) the printer/scanner response curve ( FIG. 2 ), which results in the FIG. 6C image.
- FIG. 6A luminosity is shown in FIG. 6B .
- a shadow tone correction is performed as shown in FIG. 7 .
- the solid line representing image pixels values are changed to move the pixel values toward the dashed line.
- FIG. 8 which includes a combination of darker and lighter areas. Again the FIG. 2 printer/scanner curve is used for evaluation, and again we determine that correction is needed. This time we apply tone correction shown in FIG. 9 . This correction moves pixel value from both highlights and shadows toward the preferred dashed line.
- FIG. 10 illustrates a process to help determine whether correction is needed for a particular image.
- This process can be automated using software and/or hardware.
- An image is analyzed to determine pixel values.
- the shadow region is preferably identified by a value of low luminance (e.g., see FIG. 2 ) relative to an expected workflow process. This is the lower level at which the system exhibits non-linear behavior. If not within this limit, shadow tone correction is applied.
- all image pixels are tone corrected.
- only a subset of image pixels are tone corrected.
- a predetermined number of pixels can be determined according to a number of factors, e.g., visibility constraints, watermark detection rates needed, aesthetic considerations, etc. If modification is preformed, the process can be repeated to ensure desired image characteristics. (Image modification is preferably automatically applied prior to watermark embedding, which results in more consistent watermark detection for otherwise poor images.)
- an acceptable image 10 an image including a predetermined number of pixels falling within a reasonable tonal range relative to an expected response of a workflow—passes unchanged to the printer 12 .
- FIGS. 14A and 14B illustrate a noisy image, which is smoothed ( FIGS. 15A and 15B ) prior to watermark embedding and printing.
- smoothed implies a filtering process, like a Gaussian or Weiner filter, or wavelet based filter.
- Corresponding histograms are shown in FIGS. 14C (noisy image) and 15 C (smoothed image) respectively.
- Another image analysis identifies compression artifacts that are anticipated to be introduced in a later workflow process. We correct an image to compensate for such anticipated artifacts, prior to watermark embedding.
- Another implementation of our invention allows a user to identify an expected workflow process.
- a library of response curves is queried to identify a corresponding response curve for the identified workflow process.
- a received digital image is analyzed relative to the corresponding response curve.
- Appendix A Additional digital watermarking methods and systems are disclosed in Appendix A, which is hereby incorporated by reference. Appendix A provides even further discussion of the methods, systems and apparatus discussed in the body of this application.
- a characteristic response curve of an imaging system is measured, to determine system linearity.
- Images to be watermarked are analyzed to check if a prescribed target minimum of pixels (e.g., 70% or more of the pixels) are in a linear section of the imaging system response. When this criterion is met, the image data is left unchanged. However when it is not met, a tone correction is calculated and applied to the image data so the prescribed target minimum of the pixels are in the linear portion.
- a prescribed target minimum of pixels e.g. 70% or more of the pixels
- Non-linear amplitude modifications that occur as a result of document printing and re-acquisition can pose problems of consistency in watermarking applications.
- Such non-linearities can be compensated for by characterizing the devices and making adjustments to counteract their effects. For example many manufacturers use color profiles to correct the response of the printer and image capture device (see references mentioned at the end of Appendix A).
- many manufacturers use color profiles to correct the response of the printer and image capture device (see references mentioned at the end of Appendix A).
- a watermark capture and/or print device are not adequately corrected and are outside the control of the watermarking application. In this case the captured image can be analyzed for its ability to hold a watermark, taking into account the response of the capture device and output printer.
- a set of gray patches that linearly span the range from 0 to 255 are output using the target printer and substrate.
- the patches are then re-acquired (e.g., optically scanned) and an average luminance of each patch is measured.
- the response of an example system is shown in FIG. 16 .
- saturation is seen in the highlight and shadow regions of the imaging system response curve, with an approximately linear portion that covers a range between points labeled low gray and high gray.
- FIG. 1 It can be seen from Appendix A, FIG. 1 that large modifications to a digital image in areas less than low gray or greater than high gray are mapped to small effective changes in the reacquired image. This implies that images with a large concentration of pixels in the low gray and/or high gray areas tend to require a higher watermark gain in order to achieve the same robustness as images with smaller concentrations in these regions.
- images with a large concentration of pixels in the low gray and/or high gray areas tend to require a higher watermark gain in order to achieve the same robustness as images with smaller concentrations in these regions.
- a better solution is to customize the application of the watermark for each image in the set. Although it is possible to increase watermark gain in problem areas, a better approach is to adjust low gray/high gray regions to lie within a linear range of a printer/re-acquisition system prior to applying digital watermarking.
- FIG. 18 An example of an image with saturation problems in the highlights is shown in FIG. 18 with its corresponding histogram. In the absence of tone correction, this image would require large (or intense) watermark signal gain in order for the watermark to be detected after the image is passed through the system characterized by FIG. 16 .
- FIG. 19 depicts the tone correction that is applied to the image when it is subjected to the method described in FIG. 17 . The resulting corrected image and histogram are shown in FIG. 20 .
- the overall effect of the described pre-processing methods is to reduce the contrast in problem images. If a saturation problem lies mainly with the printer, the algorithm preferably has a negligible visual impact. If, on the other hand, a capture device is the main limiter, the image contrast will appear reduced. A tradeoff here is whether noise due to watermarking for all images is more or less objectionable than reduced contrast in some images.
- SNR signal to noise ratio
- the variable i indexes all M non-zero values of a datasets' SNR distribution, which is represented by p(SNR i ).
- the variable, d(SNR i ) is the probability of not decoding the payload at the SNR level indexed by i.
- SNR distributions with higher mean and smaller variance have better robustness. Of particular impact is the portion of the SNR distribution lying below the mean because it affects robustness the most.
- problem images for watermarking can be identified. These problem images are pre-processed by applying a tone correction to the image data so that at least 70% of the pixels are in the linear portion of the imaging system. Images that already have 70% or more of the pixels in the linear portion are left unchanged.
- the automatic pre-processing of these problem images increases the robustness of the watermark in this case.
- a set of images were processed in this manner, and the watermark robustness was compared to the case when no pre-processing was applied.
- the automatic pre-processing of these problem images increases their robustness and reduces the variance in robustness of a set of watermarked images.
Abstract
Description
Claims (27)
Priority Applications (10)
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US11/143,088 US7668334B2 (en) | 2004-07-02 | 2005-06-01 | Conditioning imagery to better receive steganographic encoding |
US12/708,278 US8515120B2 (en) | 2004-07-02 | 2010-02-18 | Steganographic encoding and decoding |
US13/936,766 US8908908B2 (en) | 2004-07-02 | 2013-07-08 | Steganographic encoding and decoding |
US13/971,595 US8983120B2 (en) | 2004-07-02 | 2013-08-20 | Steganographic encoding and decoding |
US13/971,607 US8744120B2 (en) | 2004-07-02 | 2013-08-20 | Steganographic encoding and decoding |
US14/294,845 US9282216B2 (en) | 2004-07-02 | 2014-06-03 | Steganographic encoding and decoding |
US14/565,092 US9185260B2 (en) | 2004-07-02 | 2014-12-09 | Steganographic encoding and decoding |
US14/660,568 US9325878B2 (en) | 2004-07-02 | 2015-03-17 | Steganographic encoding and decoding |
US14/936,263 US9509882B2 (en) | 2004-07-02 | 2015-11-09 | Steganographic encoding and decoding |
US15/063,131 US9607350B2 (en) | 2004-07-02 | 2016-03-07 | Steganographic encoding and decoding |
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Also Published As
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US20130336522A1 (en) | 2013-12-19 |
US20160156805A1 (en) | 2016-06-02 |
US8908908B2 (en) | 2014-12-09 |
US20100322467A1 (en) | 2010-12-23 |
US9282216B2 (en) | 2016-03-08 |
US20150310576A1 (en) | 2015-10-29 |
US20130336521A1 (en) | 2013-12-19 |
US20160350889A1 (en) | 2016-12-01 |
US8983120B2 (en) | 2015-03-17 |
US8515120B2 (en) | 2013-08-20 |
US9509882B2 (en) | 2016-11-29 |
US9325878B2 (en) | 2016-04-26 |
US20060002583A1 (en) | 2006-01-05 |
US9607350B2 (en) | 2017-03-28 |
US9185260B2 (en) | 2015-11-10 |
US20140037131A1 (en) | 2014-02-06 |
US20150156368A1 (en) | 2015-06-04 |
US8744120B2 (en) | 2014-06-03 |
US20150071484A1 (en) | 2015-03-12 |
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